Soil surface ESP DSM data of the Fitzroy catchment WA, Darwin catchments and Mitchell catchment Qld generated by the Northern Australia Water Resource Assessment

Brief description

Soil surface ESP (exchangeable sodium percentage) is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Northern Australia Water Resource Assessment (NAWRA) through the digital soil mapping process (DSM). Soil surface ESP indicates chemical properties of a soil that influence soil structure stability (potential for erosion) and water infiltration. This soil surface ESP raster data represents modelled data of ESP of the soil surface (<0.10m) expressed as a percent and is derived from analysed site data and environmental covariates. Soil surface ESP is a parameter used in land suitability assessments of factors impacting water infiltration and potential erosion eg high ESP soils have reduced surface infiltration of rainfall and irrigation water. This raster data provides improved soil information used to identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO NAWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach.\nThe DSM process is described in the CSIRO NAWRA published report ‘Digital soil mapping of the Fitzroy, Darwin and Mitchell catchments. A technical report from the CSIRO Northern Australia Water Resource Assessment to the Government of Australia'. The land suitability assessment this dataset underpins is described in the CSIRO NAWRA published report ‘Land suitability of the Fitzroy, Darwin and Mitchell catchments. A technical report from the CSIRO Northern Australia Water Resource Assessment to the Government of Australia'.\nLineage: This soil surface ESP dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO NAWRA published reports and in particular 'Digital soil mapping of the Fitzroy, Darwin and Mitchell catchments. A technical report from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments. CSIRO, Australia 2018'. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create soil surface ESP Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and R squared results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For continuous attributes the method for estimating reliability is the Coefficient of Variation. This data is supplied. Method 3: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled DSM attribute value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.